An integrated probabilistic framework for shape and surface interpretation Jacob Feldman and Manish Singh, Rutgers University The representation of visual shape is one of the central problems of perception, influencing many aspects of object recognition and scene understanding. But a comprehensive and principled account of how the human brain computes shape representationsdoes not yet exists. A key difficulty is in understanding how the brain divides the image into distinct surfaces at distinct depths, interprets the 3D shape of each surface, and divides each shape into distinct parts. In previous work the PIs have developed novel, principled mathematical methods for understanding human shape representation, based around the idea of Bayesian estimation of the shape skeleton. The shape skeleton is a representation of the axial structure of the shape, related to though different from classical medial axis representations. Medial axis representations break shapes down into their component axes, about which the shape is approximately locally symmetric. The PI's approach recasts this as a probabilistic inference problem, consistent with most contemporary neurocompuational modeling, but unlike most other computational models of shape. This allows the approach to be expanded in scope, encompassing the broader problem of the decomposition of the image into distinct surfaces and the interpretation of 3D shape. The approach is theoretically unified, and is mathematically suitable to be implemented in a parallel computational architecture, making it plausible as a model of neural shape coding. The aim of this research program is to fully develop shape representation as a probabilistic es- timation problem, and test the many empirical predictions that emanate from this framework. Specific aims include expanding and testing the probabilistic approach to shape representation, and generalizing it to critical related problems, including figure/ground interpretation and 3D shape. This expansion will make it possible to fully integrate shape representation with the comprehension of surfaces in the visual image, in a coherent mathematical framework consistent with what is known about computation in visual cortex.